| economy | mean(confirmed_per_100k) | mean(mortality_rate) | mean(recovery_rate) |
|---|---|---|---|
| Commodity Based | 189.6427 | 1.929389 | 40.30342 |
| Service Based | 209.8523 | 1.386578 | 80.14083 |
| income | mean(confirmed_per_100k) | mean(mortality_rate) | mean(recovery_rate) |
|---|---|---|---|
| High income | 231.8077 | 2.2078022 | 70.84162 |
| Low income | 75.2725 | 2.5831564 | 72.18684 |
| Upper middle income | 205.3742 | 0.9272945 | 72.00805 |
| oecs | mean(confirmed_per_100k) | mean(mortality_rate) | mean(recovery_rate) |
|---|---|---|---|
| Non-OECS Member State | 327.47605 | 2.2353286 | 55.00763 |
| OECS Member State | 42.91592 | 0.5263158 | 93.73305 |
The Google Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The data allows for the tracking of movement movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.This dataset is intended to help remediate the impact of COVID-19.
Overview
This Dashboard was created in partial fulfilment of the Developing Data Products Course which comprises one of the five courses necessary for the Data Science: Statistics and Machine Learning Specialization offered by Johns Hopikins University through Coursera. This assignment challenged candidates to Create a data product and a reproducible pitch. Once completed, candidates were required to host their webpage on either GitHub Pages, RPubs, or NeoCities. The webpage presentation must contain the date that you created the document, and it must contain a plot created with Plotly.All other coursework projects completed as part of this course can be found at my GitHub repository for this course.
Rationale
The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in December 2019 in Wuhan, the capital of China’s Hubei province, and has since spread globally, resulting in the ongoing 2019–20 coronavirus pandemic. For this coursework project, I have opted to use Plotly to illustrate the spread of the Novel Coronavirus across CARICOM Member States. All CARICOM countries are classified as developing countries. They are all relatively small in terms of population and size, and diverse in terms of geography and population, culture and levels of economic and social development. While the pandemic was slow to reach the CARICOM region, the begining of March saw the onset of the pandemic among CARICOM member states.
Data Sources
With a view to map the spread of the disease thus far, I have elected to use two main data sources. Firstly, to obtain the most current data on the incidence of COVID-19, I have opted to utilise the data colelcted by the Johns Hopkins Coronavirus Resource Centre. The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE is compiled from a cross section of sources daily. To supplement this data with relevant socio-demographic data, I have opted to utilise the World Development Indicator Database maintained by the World Bank Group. The World Development Indicators is a compilation of relevant, high-quality, and internationally comparable statistics about global development and the fight against poverty. The database contains 1,600 time series indicators for 217 economies and more than 40 country groups, with data for many indicators going back more than 50 years.
Data Cleaning
A number of specialised data cleaning scripts were prepared to garner current data on a range of issues. These scripts can be found in the GitHub repository created to store the content and code generated in the completion of this course.
Developer
Yohance Nicholas | Consultant Economist @ Kairi Consultants Limited | LinkedIn | GitHub